Semantic knowledge graph embeddings for biomedical research: data integration using linked open data

Knowledge Graphs are becoming a key instrument for biomedical knowledge discovery and modeling. These approaches rely on structured data, e.g. about related proteins or genes, and form cause-and-effect networks or - if enriched with literature data and other linked data sources - knowledge graphs. A key aspect of analysis on these graphs is the missing context. Here we present a novel semantic approach towards a context enriched Knowledge Graph for biomedical research utilizing data integration with linked data. The result is a general graph concept that can be used for graph embeddings in different contexts or layers.